Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention

Samar Al-Hajj, Brian Fisher, Jennifer Smith and Ian Pike
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Samar Al-Hajj: Faculty of Health Sciences, American University of Beirut, Beirut 1107 2020, Lebanon
Brian Fisher: School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
Jennifer Smith: The BC Injury Research and Prevention Unit, BC Children’s Hospital, Vancouver, BC V6H 3V4, Canada
Ian Pike: The BC Injury Research and Prevention Unit, BC Children’s Hospital, Vancouver, BC V6H 3V4, Canada

IJERPH, 2017, vol. 14, issue 9, 1-15

Abstract: Background : Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods : Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results : The GA methodology triggered the emergence of ‘ common g round ’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusion s : Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘ common ground’ among diverse stakeholders about health data and their implications.

Keywords: group analytics; health analytics; human computer interaction; distributed cognition; collaborative visual analytics; problem solving and decision-making (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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